提出了一种基于密度网格的数据流聚类算法。
This paper introduced a density grid-based data stream clustering algorithm.
现有的数据流聚类算法无法处理高维混合属性的数据流。
Existed data stream clustering algorithms can not deal with the data stream with high-dimensional heterogeneous attributes.
目前已经提出了许多数据流聚类算法，但是都尚未解决以上数据流环境下的要求。
While a lot of clustering algorithms for data streams have been proposed, they offer no solution to the combination of these requirements.
传统的基于网格的数据流聚类算法采用固定划分网格的方法，虽然算法的处理速度较快，但是聚类准确性较低。
A kind of traditional data cluster algorithm based on grid used the method of the fixed network division, with its faster processing but low accuracy.
首先阐述了相关概念，接着提出了一种基于动态数据流挖掘的案例推理模型，其中动态数据流挖掘算法采用改进的数据流聚类算法。
This paper describes the relevant concepts and presents a model of CBR based on dynamic data stream mining, and gives an improved clustering algorithm of data stream.
数据流具有数据量无限且流速快等特点，使得传统的聚类算法不能直接应用于数据流聚类问题。
Data stream is characterized by infinite data and quick stream speed, so traditional clustering algorithm cannot be applied to data stream clustering directly.
实验表明，该算法对于解决数据流聚类问题非常有效。
Experimental results show that the algorithm is very effective to solve data stream clustering.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
实验中我们首先测试凝聚算法对网络数据流的聚类能力；
Firstly we tested the clustering ability to the network data packet of Cluster Arithmetic.
实验中我们首先测试凝聚算法对网络数据流的聚类能力；
Firstly we tested the clustering ability to the network data packet of Cluster Arithmetic.
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